Taiga provides an AI‑driven billing platform that automates the full revenue cycle for outpatient practices, mapping clinical documentation to ICD‑10, CPT and payer‑specific rules in real time while predicting claim denials. The solution generates corrective suggestions, automated appeal letters, and delivers HIPAA‑compliant analytics dashboards through secure API integrations with EHR and practice‑management systems. Practices can choose a subscription or per‑claim pricing model.
Funding
Funding not disclosed
Founders
Product
Problem
Small and independent medical practices often rely on manual revenue cycle management, leading to coding errors, high denial rates, and lengthy claim review cycles. The administrative burden reduces clinical efficiency and results in significant unrecovered revenue.
Solution
Taiga delivers an AI-native billing platform that automates the full revenue cycle from clinical documentation to payment collection. The system cross‑references notes against ICD‑10, CPT, and payer‑specific rules in real time, ensuring accurate code assignment before submission. A denial‑prediction engine flags high‑risk claims and suggests corrections, reducing the likelihood of rejections. When denials occur, the platform generates appeal letters using evidence‑based templates and payer policy references, streamlining the resubmission process. All transaction data are aggregated into a HIPAA‑compliant analytics dashboard that provides granular, payer‑level revenue insights and trend reporting. The solution integrates via secure APIs with existing EHRs and practice management systems, allowing providers to maintain their workflow while offloading billing complexity.
Target Audience
The primary customers are small to mid‑size outpatient practices—including family medicine, internal medicine, and specialty clinics—that lack dedicated revenue cycle teams and seek a cost‑effective, automated billing solution.
Features
- Real‑time validation engine that maps clinical documentation to ICD‑10, CPT, and over 6,000 payer rule sets
- Machine‑learning denial‑prediction model that scores claim risk and recommends corrective actions pre‑submission
- Automated appeal generation with configurable templates that incorporate clinical evidence and payer policy citations
- Secure, HIPAA‑compliant data pipeline with end‑to‑end encryption and audit logging for each claim transaction
- Interactive analytics portal offering drill‑down dashboards by provider, code, and payer, with exportable CSV and API endpoints
- Native EHR and practice‑management system integrations via FHIR‑compatible APIs and HL7 adapters
- Scalable cloud architecture that supports both subscription and per‑claim transaction billing models